






"""simple docstring"""





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Tuple					) ->							Union[str, Any]:
  lowerCamelCase_       	=		AutoConfig.from_pretrained(_lowerCamelCase					)
  lowerCamelCase_       	=		FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase					)
  lowerCamelCase_       	=		checkpoints.load_tax_checkpoint(_lowerCamelCase					)

  lowerCamelCase_       	=		'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

  if config.model_type == "t5":
    lowerCamelCase_       	=		'SelfAttention'
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    lowerCamelCase_       	=		'LocalSelfAttention'
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    lowerCamelCase_       	=		'TransientGlobalSelfAttention'
  else:
    raise ValueError(
        'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
        ' attribute with a value from [\'local\', \'transient-global].'					)

  # Encoder
  for layer_index in range(config.num_layers					):
    lowerCamelCase_       	=		F'''layers_{str(_lowerCamelCase					)}'''

    # Self-Attention
    lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
    lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
    lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
    lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

    # Layer Normalization
    lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

    if split_mlp_wi:
      lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
      lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
    else:
      lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

    lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

    # Layer Normalization
    lowerCamelCase_       	=		tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

    # Assigning
    lowerCamelCase_       	=		flax_model.params['encoder']['block'][str(_lowerCamelCase					)]['layer']
    lowerCamelCase_       	=		tax_attention_key
    lowerCamelCase_       	=		tax_attention_out
    lowerCamelCase_       	=		tax_attention_query
    lowerCamelCase_       	=		tax_attention_value

    lowerCamelCase_       	=		tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      lowerCamelCase_       	=		tax_global_layer_norm

    if split_mlp_wi:
      lowerCamelCase_       	=		tax_mlp_wi_a
      lowerCamelCase_       	=		tax_mlp_wi_a
    else:
      lowerCamelCase_       	=		tax_mlp_wi

    lowerCamelCase_       	=		tax_mlp_wo
    lowerCamelCase_       	=		tax_mlp_layer_norm

    lowerCamelCase_       	=		flax_model_encoder_layer_block

  # Only for layer 0:
  lowerCamelCase_       	=		tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
  lowerCamelCase_       	=		tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    lowerCamelCase_       	=		tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
    lowerCamelCase_       	=		tax_encoder_global_rel_embedding

  # Assigning
  lowerCamelCase_       	=		tax_model['target']['encoder']['encoder_norm']['scale']
  lowerCamelCase_       	=		tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers					):
    lowerCamelCase_       	=		F'''layers_{str(_lowerCamelCase					)}'''

    # Self-Attention
    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

    # Layer Normalization
    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
        'scale'
    ]

    # Encoder-Decoder-Attention
    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
    lowerCamelCase_       	=		tax_enc_dec_attention_module['key']['kernel']
    lowerCamelCase_       	=		tax_enc_dec_attention_module['out']['kernel']
    lowerCamelCase_       	=		tax_enc_dec_attention_module['query']['kernel']
    lowerCamelCase_       	=		tax_enc_dec_attention_module['value']['kernel']

    # Layer Normalization
    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

    # MLP
    if split_mlp_wi:
      lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
      lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
    else:
      lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

    # Layer Normalization
    lowerCamelCase_       	=		tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

    # Assigning
    lowerCamelCase_       	=		flax_model.params['decoder']['block'][str(_lowerCamelCase					)]['layer']
    lowerCamelCase_       	=		tax_attention_key
    lowerCamelCase_       	=		tax_attention_out
    lowerCamelCase_       	=		tax_attention_query
    lowerCamelCase_       	=		tax_attention_value

    lowerCamelCase_       	=		tax_pre_attention_layer_norm

    lowerCamelCase_       	=		tax_enc_dec_attention_key
    lowerCamelCase_       	=		tax_enc_dec_attention_out
    lowerCamelCase_       	=		tax_enc_dec_attention_query
    lowerCamelCase_       	=		tax_enc_dec_attention_value

    lowerCamelCase_       	=		tax_cross_layer_norm

    if split_mlp_wi:
      lowerCamelCase_       	=		tax_mlp_wi_a
      lowerCamelCase_       	=		tax_mlp_wi_a
    else:
      lowerCamelCase_       	=		tax_mlp_wi

    lowerCamelCase_       	=		tax_mlp_wo

    lowerCamelCase_       	=		txa_mlp_layer_norm

    lowerCamelCase_       	=		flax_model_decoder_layer_block

  # Decoder Normalization
  lowerCamelCase_       	=		tax_model['target']['decoder']['decoder_norm']['scale']
  lowerCamelCase_       	=		txa_decoder_norm

  # Only for layer 0:
  lowerCamelCase_       	=		tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
  lowerCamelCase_       	=		tax_decoder_rel_embedding

  # Token Embeddings
  lowerCamelCase_       	=		tax_model['target']['token_embedder']['embedding']
  lowerCamelCase_       	=		txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    lowerCamelCase_       	=		tax_model['target']['decoder']['logits_dense']['kernel']

  flax_model.save_pretrained(_lowerCamelCase					)
  print('T5X Model was sucessfully converted!'					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							Tuple          =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
  )
  parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
  parser.add_argument(
      '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
  )
  _SCREAMING_SNAKE_CASE   :							int          =		parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						int = 10						,							_lowerCamelCase			:						int = 1000						,							_lowerCamelCase			:						bool = True					) ->							int:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), "Invalid type of value(s) specified to function!"

  if min_val > max_val:
    raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'					)
  return min_val if option else max_val







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							int:
  return int((number_a + number_a) / 2					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							None:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), 'argument values must be type of "int"'

  if lower > higher:
    raise ValueError('argument value for lower and higher must be(lower > higher)'					)

  if not lower < to_guess < higher:
    raise ValueError(
        'guess value must be within the range of lower and higher value'					)

  def answer(_lowerCamelCase			:						int					) -> str:
    if number > to_guess:
      return "high"
    elif number < to_guess:
      return "low"
    else:
      return "same"

  print('started...'					)

  lowerCamelCase_       	=		lower
  lowerCamelCase_       	=		higher

  lowerCamelCase_       	=		[]

  while True:
    lowerCamelCase_       	=		get_avg(_lowerCamelCase						,							_lowerCamelCase					)
    last_numbers.append(_lowerCamelCase					)

    if answer(_lowerCamelCase					) == "low":
      lowerCamelCase_       	=		number
    elif answer(_lowerCamelCase					) == "high":
      lowerCamelCase_       	=		number
    else:
      break

  print(F'''guess the number : {last_numbers[-1]}'''					)
  print(F'''details : {last_numbers!s}'''					)







def 	lowerCamelCase__			(				) ->							None:
  lowerCamelCase_       	=		int(input('Enter lower value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter high value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter value to guess : '					).strip()					)
  guess_the_number(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)


if __name__ == "__main__":
  main()




